Fishing cat Prionailurus viverrinus distribution and habitat suitability in Nepal

Abstract The fishing cat Prionailurus viverrinus is a wetland specialist species endemic to South and Southeast Asia. Nepal represents the northern limit of its biogeographic range, but comprehensive information on fishing cat distribution in Nepal is lacking. To assess their distribution, we compiled fishing cat occurrence records (n = 154) from Nepal, available in published literature and unpublished data (2009–2020). Bioclimatic and environmental variables associated with their occurrence were used to predict the fishing cat habitat suitability using MaxEnt modeling. Fishing cat habitat suitability was associated with elevation (152–302 m), precipitation of the warmest quarter, i.e., April–June (668–1014 mm), precipitation of the driest month (4–7 mm), and land cover (forest/grassland and wetland). The model predicted an area of 4.4% (6679 km2) of Nepal as potential habitat for the fishing cat. About two‐thirds of the predicted potentially suitable habitat lies outside protected areas; however, a large part of the highly suitable habitat (67%) falls within protected areas. The predicted habitat suitability map serves as a reference for future investigation into fishing cat distribution as well as formulating and implementing effective conservation programs in Nepal. Fishing cat conservation initiatives should include habitats inside and outside the protected areas to ensure long‐term survival. We recommend conservation of wetland sites, surveys of fishing cats in the identified potential habitats, and studying their genetic connectivity and population status.

areas of South and Southeast Asia (Mishra et al., 2018;Mukherjee et al., 2016;Silva et al., 2020). Their range is decreasing globally with shrinking and degradation of wetlands due to several factors such as the conversion into other land uses, land degradation (increasing erosion and sedimentation), industrialization, urbanization, and global climate change (Chowdhury et al., 2015;Mishra et al., 2020;Mukherjee et al., 2012;Taylor et al., 2016). Fishing cats are also threatened by hunting, retaliatory killing, and road kills Timilsina et al., 2021).
Nepal is one of the fishing cat range countries, with the species distribution believed to encompass large parts of the Southern lowland region called the Terai (Jnawali et al., 2011). However, their actual distribution is not well understood in Nepal. Most of the information on fishing cats is based on opportunistic records during surveys targeted at large charismatic species like tigers Panthera tigris (Poudel et al., 2019;Timilsina et al., 2021;Yadav et al., 2018). Fishing cat has been recorded at eight different sites, including five protected areas (Koshi Tappu Wildlife Reserve, Parsa, Chitwan, Bardia, and Shuklaphanta National Parks) and three sites (Sunsari, Bara, and Kapilvastu) outside the protected areas in Nepal's Terai . However, the population connectivity among these sites remains unclear. Fishing cat diet broadly includes fish, rodents, birds, amphibians, and other invertebrates (Cutter, 2015;Haque & Vijayan, 1993). Along with the natural habitats, fishing cats have been recorded from agricultural fields and fish farms in all range countries (Chowdhury et al., 2015;Mishra et al., 2021;Mukherjee et al., 2016). Thus, there is the possibility of fishing cat presence in other areas of the Terai. Assessing habitat suitability is necessary to identify the potential sites within the fishing cat range and prioritize for their conservation.
Maximum entropy (MaxEnt) modeling is the most widely used method globally for predicting species distribution and habitat suitability despite its criticism as a machine learning technique (Elith et al., 2011;Guillera-Arroita et al., 2015). It uses presence-only data for predicting potential areas of the species occurrence (Phillips et al., 2006). In this study, we compiled fishing cat occurrence records together with bioclimatic and environmental variables and predicted habitat suitability of fishing cat in Nepal using maximum entropy (MaxEnt) models. Our research questions were as follows: 1. Where is fishing cat distributed in Nepal based on photographic evidence? 2. What is the extent of suitable fishing cat habitat in Nepal?
3. Which factors influence the fishing cat habitat suitability?
The predicted suitability indices from this study will guide for fishing cat conservation in Nepal.

| Study area
The study was carried out across the fishing cat range, i.e., lowland Terai region of Southern Nepal (26˚30'-28˚55'N latitude; 80˚04'-88˚08'E longitude) (Jnawali et al., 2011). The Terai is a highly productive area in the northern part of the Ganga River's floodplain. This region consists of five National Parks (Parsa, Chitwan, Banke, Bardia, and Shuklaphanta), one Wildlife Reserve (Koshi Tappu -KTWR), and a conservation area (Blackbuck) in Nepal. These protected areas (PA) of the Terai support diverse habitats including wetlands. In addition, this region also includes four Ramsar sites (wetlands of global importance), two inside (KTWR and Beeshazar) and two outside (Jagdishpur and Ghodaghodi) of the PAs.
The Terai has a sub-tropical climate with four distinct seasons: winter (mid-December/mid-March), pre-monsoon (mid-March/ mid-June), monsoon (mid-June/mid-September), and autumn (mid-September/mid-December; Lamichhane et al., 2018). May and June are the hottest months where the mean monthly temperature ranges 35-40°C. January is the coolest month, with 14-16°C in mid-winter. The mean annual rainfall ranges from 1138 to 2680 mm, with over 80% of the rain occurring during the three monsoon months (mid-June to mid-September). Until the early 1900s, most of Nepal's Terai was covered by forests, grasslands, wetlands, and rivers, but after the eradication of Malaria in the mid-1950s, large numbers of people from the hills migrated to the Terai and settled, thereby cutting down large tracts of forests for agriculture and settlements  drain into the Ganga River system in India. The Terai PAs also support many oxbow lakes created by these river systems. However, due to siltation and succession, they are gradually converted into marshes or swamps, and ultimately grasslands. These wetlands host high floral diversity (>250 species are exclusively aquatic) and provide breeding, roosting and feeding sites to many migratory and resident wetland birds, and food and habitat for fish and several invertebrates (IUCN Khadka et al., 2017;Kumar et al., 2011;Nepal, 2004). Outside the PAs a large part of the Terai of Nepal is covered by human settlements and farming lands. Rice is the most important crop of Nepal, and the Terai region produces the majority of it (Subedi & Paudel, 2020). Fish farming has been increasing in recent years, and paddy fields with a permanent water source are often converted into fish ponds (Aryal et al., 2020;Husen, 2019;Mishra et al., 2020).

| Fishing cat occurrence records
We compiled all the published data and available unpublished records of fishing cats in Nepal between 2009 and 2020, including camera trapping data, live records with photographic evidence, and carcasses of fishing cats (Table 1). We did not include pugmark records due to possible misidentification with other small cats (Palei et al., 2018). The records of fishing cats were compiled, along with the location details and GPS coordinates of each sighting. We also included details of the fishing cat images/videos such as date, time, and habitat type. A total of 312 detections of fishing cats (1 live photo capture, 2 carcasses, and 309 detections in camera traps from 1465 images and 126 videos) were compiled from 154 locations.
Of these locations, 118 were from core protected areas, 31 from the buffer zones, and 5 were outside the protected areas (Table 1).
Higher number of fishing cat presence locations inside the protected areas were obtained probably because of the high density and more survey efforts there.

| Spatial filtering of data
Spatial filtering of the fishing cats' recorded locations was carried out at the scale of 6.25 km 2 (grid cell of 2.5 × 2.5) based on the home range of a female fishing cat (4-8 km 2 ; Mishra et al., 2018;Sunquist & Sunquist, 2002). Single locations within the grid cells were extracted randomly and used in MaxEnt modeling (Kramer-Schadt et al., 2013). Of 154 locations of fishing cats, 79 remained after spatial filtering, including 64 in core areas of national parks and wildlife reserve.

| Environmental variables
We used 21 environmental variables including elevation and land cover and 19 bioclimatic variables (Table 2) (Hijmans et al., 2005). In addition, elevation and land cover data were obtained from SRTM DEM (90 m resolution) and global land cover data from DIVA-GIS, respectively (Robinson et al., 2014). The land cover included 22 different types, including forests/grasslands, wetlands, agriculture, and settlement/built up area.
All variables were converted into ASCII format as required for the modeling in MaxEnt and clipped by the boundary of Nepal in ArcGIS 10.4. First, we extracted each environmental variable corresponding to each fishing cat occurrence and ran a correlation test in SPSS 22 (Xu et al., 2019). Then, the Pearson correlation coefficient (r) was calculated and one of each pair of highly correlated (r > 0.7) variables were eliminated (Dormann et al., 2013) to improve the accuracy of the model simulation further. While eliminating the variables, we kept those more relevant to explain fishing cat distribution.
This resulted in nine variables for further MaxEnt analysis (variables in bold in Table 2).

| Distribution modeling
We used maximum entropy model in MaxEnt software 3.4.1 (Phillips et al., 2006) to predict the fishing cats' potential habitat. MaxEnt is a widely used species distribution model (SDM) for predicting species distribution when species records are available in the form of TA B L E 1 Presence data of fishing cats between 2009 and 2020 with number of location (and detections in parenthesis) used in this study and their sources ("NA" means "information not available"). Unpublished data from camera trapping was obtained during tiger surveys in respective sites presence-only data (Elith et al., 2011;Xu et al., 2019). This model provides the probability of occurrence of a given species, ranging from 0 to 1, and the closer the value is to 1, the greater the probability of species occurrence (Phillips et al., 2006).

| Key environmental variables contributing to fishing cat distribution
The ROC of the constructed MaxEnt model (Figure 3) showed the AUC value of 0.990, indicating excellent performance of the model. Collectively, these nine variables contributed for 99% of the variation in model. Four variables were responsible for predicting >5% of the variation in the data (Table 3). Elevation was the most important variable with 32.3% contribution, followed by precipitation of the warmest quarter (18_bio), precipitation of the driest month (14_bio), and land cover. Five variables: mean temperature of wettest quarter (8_bio); annual mean temperature (1_bio); precipitation of coldest quarter (19_bio); minimum temperature of coldest month (6_bio); and mean diurnal range (2_bio) contribute the least (each below 5%) to the distribution of the species (Table 3).
The results indicated that the highest regularized training gain (1.8) for the fishing cat in the present model occurred when the minimum temperature of the coldest month (6_bio) was used in isolation for running the model (Figure 4). This variable is followed in importance by elevation; annual mean temperature (1_bio); mean temperature of the wettest quarter (8_bio); and precipitation of the warmest quarter (18_bio) with the regularized training gain >1.0. The order of importance of the remaining key environmental factors is the precipitation of the driest month (14_bio); mean diurnal range (mean of monthly (maximum temperature -minimum temperature)) (2_bio); precipitation of coldest quarter (19_bio); and land cover. The training sample gain was the lowest after omitting the precipitation of the driest month (14_bio) from the model, suggesting its crucial role in identifying suitable habitat for the fishing cat (Figure 4).

| Predicted habitat suitability
The model predicted a total of 6679 km 2 of Nepal as potential habitat for fishing cats ( Figure 5). Of this, a 992 km 2 area was predicted as highly suitable, 1881 km 2 as moderately suitable, and 3806 km 2 as less suitable (Table 4). A large part of Nepal (141,733 km 2 ) was predicted as unsuitable for the fishing cat. The suitable range of environmental variables for fishing cat distribution based on response curves (Appendix S1) in the MaxEnt model was calculated and presented in Table 5.

| DISCUSS ION
This is the first comprehensive study of fishing cat distribution and prediction of their potential habitat in Nepal. Only 4.4% of Nepal was found to be potential fishing cat habitat, mostly in the lowland Terai (<300 m). Despite a few fishing cat presence locations, the majority (65%) of the potentially suitable habitat lies outside the PAs. However, two-thirds of the "highly suitable" habitat lies within PAs ( Figure 5). The fishing cat population within the PAs receives the highest level of protection. Among the protected areas, the highest number of fishing cat records were obtained from Shuklaphanta National Park, followed by Koshi Tappu Wildlife Reserve, indicating a high density of fishing cats in those sites . In contrast, there were only a few records outside the PAs indicating less survey effort or their low density ( Table 1). Our results show that elevation, precipitation of driest months, precipitation of the warmest quarter, and land cover are the most important variables predicting the fishing cat habitat suitability.
Furthermore, the jackknife test of variable importance shows that the environmental variable with the highest gain when used in isolation is the "Min Temperature of the Coldest Month." The environmental variable that decreases the gain the most when it is omitted (have the most information that is not present in the other variables) is the "Precipitation of the Driest Month." We found fishing cats below 310 m in the lowland Terai of Nepal, and similar records are reported throughout the range. However, in Sri Lanka, the fishing cat has been recorded up to 1800 m altitude (Mukherjee et al., 2016). Precipitation in the driest month and the wettest quarter had high contributions for predicting the fishing cat distribution. Similar findings on the influence of precipitation of the driest month have been reported in Bangladesh (Rahman, 2017). It indicates that precipitation is important for the higher suitability of fishing cat habitat as it can temporarily expand the wetland area during flooding.
The narrow suitable range of these predictor variables indicates the narrow environmental niche of fishing cats. Therefore, alteration in temperature and precipitation will potentially affect fishing cats' niche. Amid global climate change, the annual average, average minimum, and average maximum temperature of Nepal's Terai are increasing. In contrast, the annual average precipitation is decreasing, with decreases in both pre-monsoon and monsoon precipitation in recent years (Thapa & Dhulikhel, 2019). Such trend is expected to continue, which could reduce the suitable range of fishing cats in future.  The model showed the vital role of wetlands, and forest and grassland cover for the occurrence of fishing cats (Mishra et al., 2018;Palei et al., 2018). However, the freshwater wetland ecosystem is vulnerable to various threats such as shrinkage, decreasing water volume, the spread of invasive species, physical/chemical pollution, and climate change (Chaudhary et al., 2016;Lamsal et al., 2017).
Wetland surveys in Chitwan documented drying of some wetlands (converted into grasslands or forests) and one-quarter of them were in bad condition (Khadka et al., 2015), affecting fishing cat distribution. The first author failed to obtain fishing cats in camera traps (2012) from a location in the eastern sector of Chitwan NP where four fishing cat individuals were radio-collared during the 1980s (Mishra, 2013;Mishra et al., 2021;Sunquist & Sunquist, 2002). The marshes at this location in the 1980s have since completely dried and converted into grasslands. This is an example of the rapid habitat conversion threatening wetland specialist species like fishing cats.  (Kolipaka et al., 2019). In Thailand, Cutter (2015) reported extensive use of paddy fields by fishing cats. In Sri Lanka, fishing cats also occur in peri-urban areas of Colombo (Ratnayaka, 2021). In Nepal Terai, fisheries are expanding at the expense of agricultural areas, creating both opportunities (additional wetland habitats with abundant fish) and challenges (risks of retaliatory killing in the fish ponds) .

| CONS ERVATION IMPLIC ATIONS AND CON CLUS ION
This study has comprehensively presented fishing cat records in Nepal from recent decade, and predicted habitat suitability through maximum entropy model. Our finding is important for conservation planning and prioritization of areas for fishing cat conservation in Nepal. Only a small portion of Nepal's Terai is predicted as potential fishing cat habitat. The majority of predicted fishing cat habitat lie outside the protected areas where they face various threats for survival, including habitat conversion (into agriculture, settlement, and other infrastructure development), persecution, and poaching.
In addition, wetland shrinkage and habitat conversion caused by the changing global climate threaten this habitat specialist. Therefore, we recommend investigation of the status of fishing cats in Nepal and possible connectivity among population clusters of this species, and conservation awareness for local stakeholders and communities in predicted potential sites.

ACK N OWLED G EM ENTS
We thank the Department of National Parks and Wildlife